The T16 Planet Hunt: 10,000 New Planet Candidates from TESS Cycle 1 and the Confirmation of a Hot Jupiter Around TIC 183374187

TL;DR

The T16 project processed TESS full-frame images to identify over 11,500 new transiting exoplanet candidates, including a confirmed hot Jupiter, significantly expanding the catalog of potential exoplanets around faint stars.

Contribution

This work introduces a semi-automated pipeline that detects new exoplanet candidates from TESS data, more than doubling the known TESS candidates and demonstrating successful validation with radial-velocity follow-up.

Findings

Identified 10,091 new planet candidates around faint stars.

Confirmed a hot Jupiter around TIC 183374187.

Pipeline effectively detects real transiting planets in TESS data.

Abstract

The T16 project has produced a uniformly detrended and systematics-corrected set of 83,717,159 TESS Cycle 1 full-frame image light curves for stars observed by TESS in its primary mission down to T=16 mag, enabling sensitive transit searches beyond the official TESS pipelines. While most existing TESS planet searches focus on relatively bright targets, planet occurrence rates suggest that a substantial number of planets should exist around fainter stars. We therefore use the T16 light curves to conduct a semi-automated search for transiting exoplanets across the full Cycle 1 FFI sample, resulting in 11,554 planet candidates orbiting stars down to 16th magnitude in the TESS band with orbital periods between 0.5 and 27 days. Of these, 10,091 are new planet candidates, and 411 are single-transit events, for which we do not attempt to determine orbital parameters. The remaining 1,052 candidates are previously known TESS candidates. We validate our pipeline through Magellan/PFS radial-velocity follow-up measurements on one of our candidate hosts, TIC 183374187, a metal poor thick-disk star, confirming the signal as newly identified hot Jupiter. This detection demonstrates our pipeline's ability to identify real, previously undiscovered, transiting planets. Overall, this work shows that large-scale, machine learning-assisted transit searches of TESS full-frame images can significantly expand the census of transiting planet candidates, particularly around faint stars, providing a rich target set for future validation and follow-up efforts. Our findings more than double the number of known TESS exoplanet candidates.